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Fenotipado Digital para la Salud Mental Adolescente: Estudio de Viabilidad Utilizando Aprendizaje Automático para

Balasundaram Kadirvelu1, Teresa Bellido Bel2, Aglaia Freccero2

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El fenotipado digital con teléfonos inteligentes predice eficazmente los riesgos de salud mental en adolescentes al integrar datos activos y pasivos. Este enfoque escalable ayuda a la detección temprana en jóvenes no clínicos, cerrando una brecha crítica en la prevención comunitaria.

Palabras clave:
EMAinteligencia artificialsalud digitalintervención tempranaevaluación ecológica momentáneamHealthaplicaciones móvilessalud móvildetección de teléfonos inteligentessalud mental juvenil

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Área de la Ciencia:

  • Salud digital
  • Aprendizaje automático en salud mental
  • Psicología del adolescente

Sus antecedentes:

  • Los adolescentes son muy vulnerables a los trastornos mentales, con una aparición típicamente antes de los 25 años.
  • La mayoría de los adolescentes con síntomas de salud mental no buscan ayuda profesional.
  • El fenotipado digital ofrece un método de baja carga para la detección temprana de riesgos en jóvenes.

Objetivo del estudio:

  • Evaluar la viabilidad de una aplicación de teléfono inteligente para predecir riesgos de salud mental en adolescentes no clínicos.
  • Integrar datos activos (autoinformados) y pasivos (sensores) utilizando aprendizaje automático.
  • Identificar riesgos de dificultades internalizantes/externalizantes, trastornos alimentarios, insomnio e ideación suicida.

Principales métodos:

  • 103 adolescentes usaron la aplicación Mindcraft durante 14 días, recopilando autoinformes diarios y datos de sensores pasivos.
  • Se desarrolló un modelo de aprendizaje profundo con preentrenamiento contrastivo para la clasificación binaria de resultados de salud mental.
  • El rendimiento se evaluó utilizando validación cruzada leave-one-subject-out y se comparó con otros modelos de ML.

Principales resultados:

  • La integración de datos activos y pasivos logró precisiones equilibradas de 0.67-0.77 en los resultados de salud mental.
  • El enfoque de aprendizaje contrastivo mejoró la estabilidad del modelo y la robustez predictiva.
  • Las explicaciones aditivas de Shapley (SHAP) identificaron características clínicamente relevantes, confirmando el valor de la integración de datos.

Conclusiones:

  • El fenotipado digital basado en teléfonos inteligentes es factible y útil para predecir riesgos de salud mental en adolescentes no clínicos.
  • Este enfoque de datos integrados muestra una promesa para la detección temprana y las intervenciones escalables.
  • Los hallazgos respaldan los esfuerzos de prevención basados en la comunidad para la salud mental de los adolescentes.